


'''simple docstring'''



import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


A__						:   Tuple				=    random.Random()

if is_torch_available():
      import torch



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							Optional[Any]=1.0 ,       UpperCAmelCase_       :							List[str]=None ,       UpperCAmelCase_       :							int=None				)	->						Optional[int]:





     if rng is None:
          __lowerCamelCase  :      Dict														=						global_rng

     __lowerCamelCase  :      int														=						[]
     for batch_idx in range(shape[0]				):
          values.append([]				)
          for _ in range(shape[1]				):
               values[-1].append(rng.random() * scale				)

     return values





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=4_00     ,	SCREAMING_SNAKE_CASE_=20_00     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=1_60_00     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	)							->	Tuple:
         __lowerCamelCase  :      str														=						parent
         __lowerCamelCase  :      Any														=						batch_size
         __lowerCamelCase  :      Union[str, Any]														=						min_seq_length
         __lowerCamelCase  :      str														=						max_seq_length
         __lowerCamelCase  :      Tuple														=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __lowerCamelCase  :      str														=						feature_size
         __lowerCamelCase  :      int														=						padding_value
         __lowerCamelCase  :      Optional[int]														=						sampling_rate
         __lowerCamelCase  :      Optional[int]														=						return_attention_mask
         __lowerCamelCase  :      Any														=						do_normalize







    def  lowercase_							(				self      )							->	str:
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False      )							->	List[str]:

         def _flatten(SCREAMING_SNAKE_CASE_      ):
              return list(itertools.chain(*UpperCamelCase_      )      )

         if equal_length:
              __lowerCamelCase  :      int														=						floats_list((self.batch_size, self.max_seq_length)      )
         else:
              # make sure that inputs increase in size
              __lowerCamelCase  :      Union[str, Any]														=						[
                  _flatten(floats_list((x, self.feature_size)      )      )
                  for x in range(self.min_seq_length     ,	self.max_seq_length     ,	self.seq_length_diff      )
              ]

         if numpify:
              __lowerCamelCase  :      int														=						[np.asarray(UpperCamelCase_      ) for x in speech_inputs]

         return speech_inputs







@require_torch
@require_torchaudio
class   UpperCAmelCase_    (a_   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   str					=     ASTFeatureExtractor







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Any														=						ASTFeatureExtractionTester(self      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      List[Any]														=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
         # create three inputs of length 800, 1000, and 1200
         __lowerCamelCase  :      Union[str, Any]														=						[floats_list((1, x)      )[0] for x in range(8_00     ,	14_00     ,	2_00      )]
         __lowerCamelCase  :      str														=						[np.asarray(UpperCamelCase_      ) for speech_input in speech_inputs]

         # Test not batched input
         __lowerCamelCase  :      List[str]														=						feat_extract(speech_inputs[0]     ,	return_tensors='np'      ).input_values
         __lowerCamelCase  :      Optional[int]														=						feat_extract(np_speech_inputs[0]     ,	return_tensors='np'      ).input_values
         self.assertTrue(np.allclose(UpperCamelCase_     ,	UpperCamelCase_     ,	atol=1E-3      )      )

         # Test batched
         __lowerCamelCase  :      Any														=						feat_extract(UpperCamelCase_     ,	padding=UpperCamelCase_     ,	return_tensors='np'      ).input_values
         __lowerCamelCase  :      List[str]														=						feat_extract(UpperCamelCase_     ,	padding=UpperCamelCase_     ,	return_tensors='np'      ).input_values
         for enc_seq_a, enc_seq_a in zip(UpperCamelCase_     ,	UpperCamelCase_      ):
              self.assertTrue(np.allclose(UpperCamelCase_     ,	UpperCamelCase_     ,	atol=1E-3      )      )

         # Test 2-D numpy arrays are batched.
         __lowerCamelCase  :      Optional[Any]														=						[floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
         __lowerCamelCase  :      List[str]														=						np.asarray(UpperCamelCase_      )
         __lowerCamelCase  :      Optional[Any]														=						feat_extract(UpperCamelCase_     ,	return_tensors='np'      ).input_values
         __lowerCamelCase  :      str														=						feat_extract(UpperCamelCase_     ,	return_tensors='np'      ).input_values
         for enc_seq_a, enc_seq_a in zip(UpperCamelCase_     ,	UpperCamelCase_      ):
              self.assertTrue(np.allclose(UpperCamelCase_     ,	UpperCamelCase_     ,	atol=1E-3      )      )







    @require_torch
    def  lowercase_							(				self      )							->	Optional[int]:
         import torch

         __lowerCamelCase  :      List[str]														=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
         __lowerCamelCase  :      str														=						np.random.rand(1_00      ).astype(np.floataa      )
         __lowerCamelCase  :      Any														=						np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
              __lowerCamelCase  :      Optional[int]														=						feature_extractor.pad([{'input_values': inputs}]     ,	return_tensors='np'      )
              self.assertTrue(np_processed.input_values.dtype == np.floataa      )
              __lowerCamelCase  :      List[Any]														=						feature_extractor.pad([{'input_values': inputs}]     ,	return_tensors='pt'      )
              self.assertTrue(pt_processed.input_values.dtype == torch.floataa      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         from datasets import load_dataset

         __lowerCamelCase  :      Union[str, Any]														=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,	'clean'     ,	split='validation'      )
         # automatic decoding with librispeech
         __lowerCamelCase  :      Optional[int]														=						ds.sort('id'      ).select(range(UpperCamelCase_      )      )[:num_samples]['audio']

         return [x["array"] for x in speech_samples]







    @require_torch
    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Union[str, Any]														=						torch.tensor(
             [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
              -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
              -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
              -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]      )
         # fmt: on

         __lowerCamelCase  :      List[Any]														=						self._load_datasamples(1      )
         __lowerCamelCase  :      Any														=						ASTFeatureExtractor()
         __lowerCamelCase  :      Optional[Any]														=						feature_extractor(UpperCamelCase_     ,	return_tensors='pt'      ).input_values
         self.assertEquals(input_values.shape     ,	(1, 10_24, 1_28)      )
         self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,	UpperCamelCase_     ,	atol=1E-4      )      )














"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"